code: def nms(boxes, scores, iou_threshold):
      # type: (Tensor, Tensor, float) -> Tensor
      """
      Performs non-maximum suppression (NMS) on the boxes according
      to their intersection-over-union (IoU).
  
      NMS iteratively removes lower scoring boxes which have an
      IoU greater than iou_threshold with another (higher scoring)
      box.
  
      Parameters
      ----------
      boxes : Tensor[N, 4])
          boxes to perform NMS on. They
          are expected to be in (x1, y1, x2, y2) format
      scores : Tensor[N]
          scores for each one of the boxes
      iou_threshold : float
          discards all overlapping
          boxes with IoU > iou_threshold
  
      Returns
      -------
      keep : Tensor
          int64 tensor with the indices
          of the elements that have been kept
          by NMS, sorted in decreasing order of scores
      """
      return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/train_coco_dataset/network_files/boxes.py
imports: import torch
  from typing import Tuple
  from torch import Tensor
  import torchvision
label_desc: Use image node_id 1 for calling a global function with example usage: nms(boxes, scores, iou_threshold) and returns: torch